Scaling Responsible AI for Equitable Maternal Healthcare Across the MENA Region — AI-assisted fetal ultrasound analysis, evidence-based AI insights, automated reporting, and public health awareness, built upon the validated FADA Phase I foundation.
Maternal healthcare systems across the MENA region face critical challenges including diagnostic variability, expertise shortages, equipment limitations, documentation burden, and unequal healthcare access.
AI Scholar MaternaCare addresses these challenges by combining AI-assisted fetal ultrasound interpretation with explainable clinical intelligence and workflow automation — supporting clinicians, not replacing them.
Frontline maternal healthcare facilities often operate under constrained conditions that compound into real risks for mother and baby.
AI Scholar MaternaCare is designed to augment — not replace — clinical expertise through responsible, human-centered AI support.
The MaternaCare scale-up builds on the validated success of the FADA (Fetal Anomaly Detection Algorithm) Phase I pilot at HBKU.
Fetal ultrasound images assembled and harmonized
Brain structure segmentation accuracy
Signal-to-noise improvement using GAN enhancement
Expert clinical rating for anatomical accuracy and relevance
AI Scholar MaternaCare integrates three core layers — imaging intelligence, evidence-based reasoning, and clinical & public-health output.
Processes fetal ultrasound scans and extracts clinically relevant insights.
Combines Large Language Models with Retrieval-Augmented Generation grounded in evidence-based medical knowledge.
Generates explainable outputs for both clinicians and community-level outreach.
Six capabilities that turn fetal ultrasound scans into structured, evidence-based clinical and public health intelligence.
Automated fetal scan analysis and anatomical structure identification.
Explainable, structured medical reports generated with RAG-powered evidence retrieval.
Reduce manual reporting burden and administrative overhead for busy clinicians.
Transform clinical insights into accessible awareness materials for maternal-health outreach.
Transparent AI outputs designed to support clinician trust and accountability.
Scalable and secure deployment across healthcare institutions, with audit-ready logging.
AI Scholar MaternaCare follows a human-centered governance model — clinicians remain in full authority of every diagnostic decision.
Clinicians retain full authority over diagnosis and decision-making.
De-identification protocols, encryption-based transmission, and institutional compliance standards.
Full auditability, clinical governance boundaries, and ethical review mechanisms.
Designed to empower maternal healthcare providers while preserving patient dignity and cultural sensitivity.
The platform incorporates diverse training datasets, real-time performance dashboards, subgroup bias auditing, and explainable AI outputs — under continuous monitoring.
Stable performance across institutions and operators.
Bias monitoring across patient subgroups.
Adaptive across legacy and modern ultrasound equipment.
Explanations accompany every AI output.
Designed for sustainable institutional integration — operationally, financially, and regionally adaptable across MENA healthcare systems.
Three intersecting dimensions of impact — clinical, systemic, and societal.
Standardized diagnostic support across diverse healthcare environments.
Reduced administrative burden, freeing clinicians to focus on patient care.
Equitable digital maternal-healthcare access across the MENA region.
